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Application of neural network in metal adsorption using biomaterials (BMs): a review
Citation:
Nighojkar, A., K. Zimmermann, M. Ibrahim, B. Barbeau, M. Mohseni, S. Krishnamurthy, F. Dixit, AND B. Kandasubramanian. Application of neural network in metal adsorption using biomaterials (BMs): a review. Environmental Science: Advances. Royal Society of Chemistry, London, Uk, 2(1):11-38, (2023). https://doi.org/10.1039/D2VA00200K
Impact/Purpose:
ANNs for simulating metal ions adsorption on biopolymers are reviewed. ANNs displayed better predictive performance than statistical regression models.
Description:
With growing environmental consciousness, biomaterials (BMs) have garnered attention as sustainable materials for the adsorption of hazardous water contaminants. These BMs are engineered using surface treatments or physical alterations to enhance their adsorptive properties. The lab-scale methods generally employ a One Variable at a Time (OVAT) approach to analyze the impact of biomaterial modifications, their characteristics and other process variables such as pH, temperature, dosage, etc., on the removal of metals via adsorption. Although implementing the adsorption procedure using BMs seems simple, the conjugate effects of adsorbent properties and process attributes implicate complex nonlinear interactions. As a result, artificial neural networks (ANN) have gained traction in the quest to understand the complex metal adsorption processes on biomaterials, with applications in environmental remediation and water reuse.
URLs/Downloads:
DOI: Application of neural network in metal adsorption using biomaterials (BMs): a review
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